Is Python/Django a Good Tech Stack for Data Wranglers?
The Python/Django tech stack facilitates rapid development and is an excellent multi-purpose programming language. But can data wranglers use it as their main tool?
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The Python and Django tech stack is very popular among developers and data wranglers. Data wranglers convert data mapping and transformation from a raw format to a more usable format. And it doesn’t end there. They also take care of other data operations such as data visualization, building statistical models, data aggregations, etc.
The Python/Django tech stack is modern, facilitates rapid development, and is an excellent overall multi-purpose programming language used in many industries and academia. However, the question remains: Can data wranglers use it as the main tool for their work?
Data visualization and data analytics projects are heavily data focused. There are two types of projects one can do: product-based and script-based. If a team is building a product for users, they should look at what the Python/Django tech stack has to offer. Any product-based data project requires a proper user interface, and this is where the Python/Django comes in.
Interactive Data Products
Data science projects go through similar steps, i.e. gathering raw data, data quality assurance, creating the data pipeline, working on a model, and getting the desired output. Interactivity is not desirable for all projects. However, some projects require interactivity. Interactive data projects are different and require proper user interactivity.
Without Django or other similar technology, it is not possible to pull it off. One of the best examples of interactive data products is a recommendation system. Amazon heavily uses a recommendation system, making it easy for end-users to consume and make sense of complex data. For example, when you search for books on Amazon, the recommendation system tries to find books that might interest you. Recommendation systems are complex and process a lot of information before suggesting something to you. For a user, it's not possible to make sense of the data generated by a recommendation system. That’s where the Django or another programming language can come in to help designers showcase information in a meaningful way.
Where Does Django/Python Stand as a Tech Stack for Interactive Data Projects?
In the end, the tech stack can prove to be a valuable tool for data wranglers.
So, Where Can You Get Started?
Now that you are convinced that the Django/Python tech stack can help you make better user-based data application, let’s look at best online projects that can help you improve your skills.
We will make it easier for you by listing the best tutorials that you can follow to improve your understanding of the Django/Python tech stack.
Python Data Analytics and Visualization is a live project that covers all the resources, tools, and languages needed to create an analytics dashboard. The tutorial covers fundamentals and advanced techniques for analyzing and visualizing datasets. Even if you're not a beginner, you can learn from the tutorial. This project gives you a clear look at how the Django/Python tech stack is useful for data visualization and data analytics.
Building dashboards with Django and D3: Building a dashboard is one of the major parts of an interactive data product. In this tutorial, you will learn how to create a fully functional dashboard with help from D3 and Django.
Django as a way to reporting and research tool: In this GitHub tutorial, you will learn how to load data from a CSV into Django models, create views and queries for data analysis, and create visualizations and tables for publication purposes.
Interactive data visualization using Bokeh: Bokeh is a popular Python library for creating interactive data visualizations. It offers good advantages for your data visualization project.
Cool libraries and the ability to create interactive web projects make Django a good option for data projects as well as web development. Python plays a core role in making Django a good web framework for data science projects.
So, what do you think about the tech stack? Can it be used for data science projects? Comment below and let us know.
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